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Architects face the challenge of exploring various design solutions in the early design stage, often with conflicting optimization goals. To tackle this complexity, they need to rely on tools and methodologies during the conceptual phase to assess and optimize designs, considering multiple aspects of building performance. Parametric Design, Generative Design, and automation in Building Information Modelling (BIM) offer architects new opportunities to work on complex buildings. These advancements empower designers to enhance their designs, increase project efficiency, improve performance, and reduce project time and costs. Multi-Objective Optimization algorithms are employed to address conflicting objectives in the design process. The GENIUS project introduces an Algorithm-Aided Design workflow that optimizes the building shape and Window-to-Wall Ratio of an office building, considering energy and daylight performance. The integration of BIM software, visual programming tools, and Artificial Intelligence techniques (Genetic Algorithms and RBFOpt model-based optimization) allows architects to identify optimal solutions aligning with design objectives. The workflow was validated through a case study of a large office building, focusing on maximizing daylight performance using the Spatial Daylight Autonomy metric and minimizing energy consumption using the Energy Use Intensity metric. The GENIUS project equips architects with a methodology and toolset to improve their designs and identify optimal solutions for complex design challenges. [ABSTRACT FROM AUTHOR]

This paper explores the influence of different turbocharging modes and multi-parameter coordinated control on the performance of compression-ignition (CI) aviation piston engine (APE). Firstly, based on a constructed one-dimensional thermodynamic model of a CI APE, the study investigates the effects of different turbocharging modes and combinations of high and low-pressure stage variable geometry turbines (VGT) on the operational performance of the engine. Subsequently, a novel stepwise approximate multi-objective optimization algorithm is proposed, combining backpropagation neural networks and the non-dominated sorting genetic algorithm II. This algorithm evaluates the influence of multiple control parameters on a two-stage turbocharged engine's performance, achieving a balance between fuel economy and reliability. The research shows that equipping CI APEs with twin VGT for supercharging can notably enhance engine performance, enabling the achievement of favorable power recovery objectives at an altitude of 8000 m. The proposed optimization algorithm exhibits strong predictability and reliability, substantially accelerating the computation speed and reducing the data volume by approximately 95%. At an engine speed of 3887 rpm, compared to the unoptimized conditions, the brake specific fuel consumption of the best scenario at altitudes of 2000 m, 4000 m, 6000 m, and 8000 m is reduced by 7.1%, 7.2%, 8.6%, and 6.9%, respectively. • Influences of different turbocharging modes on the performance of a CI APE are investigated. • A novel stepwise approximate multi-objective optimization method based on a hybrid model is proposed. • Explored the altitude-varying control strategy under coordinated multi-parameter optimization. • Increased the altitude ceiling for the CI APE, reaching 82% power recovery target at 8000 m. [ABSTRACT FROM AUTHOR]